{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
